Machine Vision-Based Surface Defect Detection Study for Ceramic 3D Printing

Abstract

A set of online inspection systems for surface defects based on machine vision was designed in response to the issue that extrusion molding ceramic 3D printing is prone to pits, bubbles, bulges, and other defects during the printing process that affect the mechanical properties of the printed products. The inspection system automatically identifies and locates defects in the printing process by inspecting the upper surface of the printing blank, and then feeds back to the control system to produce a layer of adjustment or stop the printing. Due to the conflict between the position of the camera and the extrusion head of the printer, the camera is placed at an angle, and the method of identifying the points and fitting the function to the data was used to correct the camera for aberrations. The region to be detected is extracted using the Otsu method (OSTU) on the acquired image, and the defects are detected using methods such as the Canny algorithm and Fast Fourier Transform, and the three defects are distinguished using the double threshold method. The experimental results show that the new aberration correction method can effectively minimize the effect of near-large selection caused by the tilted placement of the camera, and the accuracy of this system in detecting surface defects reached more than 97.2%, with a detection accuracy of 0.051 mm, which can meet the detection requirements. Using the weighting function to distinguish between its features and defects, and using the confusion matrix with the recall rate and precision as the evaluation indexes of this system, the results show that the detection system has accurate detection capability for the defects that occur during the printing process.